Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder
In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and lo...
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| Vydáno v: | Applied soft computing Ročník 74; s. 1 - 9 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2019
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and long prediction time. We first propose using an unsupervised three-layer nonnegativity-constrained autoencoder to adaptive search the optimal size of the sliding window, then construct a deep nonnegativity-constrained autoencoder network to automatically extract hidden features of driving behavior to further complete driver identification. The results from the public driving behavior dataset indicate that relative to conventional sparse autoencoder, dropout-autoencoder, random tree, and random forest algorithms, our method can effectively search the optimal size of the sliding time window, and the window size is shortened from the traditional 60s to 30s, which can better preserve the intrinsic information of the data while greatly reducing the data volume. Furthermore, our method can extract more distinctive hidden features that aid the classifier to map out the separating boundaries among the classes more easily. Finally, our method can significantly shorten the prediction time and improve the timeliness under the premise of improving the driver identification performance and reducing the model overfitting.
[Display omitted]
•We proposed a three-layer autoencoder to adaptively search time window size.•We constructed a deep autoencoder to automatically extract the hidden features.•The proposed method is superior to the existing state-of-the-art methods. |
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| AbstractList | In this paper, we propose a new driver identification method using deep learning. Existing driver identification methods have the disadvantages that the size of the sliding time window is too large and the feature extraction is relatively subjective, which leads to low identification accuracy and long prediction time. We first propose using an unsupervised three-layer nonnegativity-constrained autoencoder to adaptive search the optimal size of the sliding window, then construct a deep nonnegativity-constrained autoencoder network to automatically extract hidden features of driving behavior to further complete driver identification. The results from the public driving behavior dataset indicate that relative to conventional sparse autoencoder, dropout-autoencoder, random tree, and random forest algorithms, our method can effectively search the optimal size of the sliding time window, and the window size is shortened from the traditional 60s to 30s, which can better preserve the intrinsic information of the data while greatly reducing the data volume. Furthermore, our method can extract more distinctive hidden features that aid the classifier to map out the separating boundaries among the classes more easily. Finally, our method can significantly shorten the prediction time and improve the timeliness under the premise of improving the driver identification performance and reducing the model overfitting.
[Display omitted]
•We proposed a three-layer autoencoder to adaptively search time window size.•We constructed a deep autoencoder to automatically extract the hidden features.•The proposed method is superior to the existing state-of-the-art methods. |
| Author | Chen, Jie Wu, ZhongCheng Zhang, Jun |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0002-9605-4331 surname: Chen fullname: Chen, Jie email: cj2016@mail.ustc.edu.cn organization: Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China – sequence: 2 givenname: ZhongCheng surname: Wu fullname: Wu, ZhongCheng email: zcwu@iim.ac.cn organization: Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China – sequence: 3 givenname: Jun surname: Zhang fullname: Zhang, Jun email: zhang_jun@hmfl.ac.cn organization: Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China |
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| Keywords | Driver identification Deep learning Adaptive search Feature extraction Nonnegativity-constrained autoencoder |
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| Title | Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder |
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